Information Recovery in a Dynamic Statistical Markov Model
AbstractAlthough economic processes and systems are in general simple in nature, the underlying dynamics are complicated and seldom understood. Recognizing this, in this paper we use a nonstationary-conditional Markov process model of observed aggregate data to learn about and recover causal influence information associated with the underlying dynamic micro-behavior. Estimating equations are used as a link to the data and to model the dynamic conditional Markov process. To recover the unknown transition probabilities, we use an information theoretic approach to model the data and derive a new class of conditional Markov models. A quadratic loss function is used as a basis for selecting the optimal member from the family of possible likelihood-entropy functional(s). The asymptotic properties of the resulting estimators are demonstrated, and a range of potential applications is discussed. View Full-Text
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Miller, D.J.; Judge, G. Information Recovery in a Dynamic Statistical Markov Model. Econometrics 2015, 3, 187-198.
Miller DJ, Judge G. Information Recovery in a Dynamic Statistical Markov Model. Econometrics. 2015; 3(2):187-198.Chicago/Turabian Style
Miller, Douglas J.; Judge, George. 2015. "Information Recovery in a Dynamic Statistical Markov Model." Econometrics 3, no. 2: 187-198.